The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for detecting hotspots using machine learning on diffraction patterns.
Optical lithography is a crucial step in semiconductor manufacturing. The basic principle of optical lithography is quite similar to that of chemistry-based photography. The images of the patterned photo-mask are projected through the high-precision optical system onto the wafer surface, which is coated with a layer of light-sensitive chemical compound, e.g. photo-resist. The patterns are then formed on the wafer surface after complex chemical reactions and follow-on manufacturing steps, such as development, post-exposure bake, and wet or dry etching.
Low k1 lithography presents significant printability challenges for 22 nm technology. Design rules must guarantee manufacturable layouts over all possible enumerations of the design rule checker (DRC) clean shapes. The number of rules must be within a practical limit while still covering a wide range of complex two-dimensional optical interactions.
A lithographic hotspot is an area of the design that is likely to produce a printing error. The number of lithographic hotspots is growing exponentially with further scaling into low k1 photolithography. This is because design rules are no longer adequate for guaranteeing printability of designs. Lithographic hotspots are most prominent in bi-directional layers like 1× metal. Hotspots cause design/process churn. It is critical to identify and eliminate hotspots early in the design process to reduce design/manufacturing costs.